Super-pixel detection for wearable diffuse optical tomography

ABSTRACT

A system includes a wearable head apparatus and an electronic console. The head apparatus is configured to receive resultant light from the head of a subject. The electronic console includes a fiber array, a detector, and a computing device. The fiber array includes a plurality of fibers configured to transport resultant light received by the head apparatus. The detector includes a plurality of super-pixels each defined by a plurality of pixels of an array of pixels. Each super-pixel is associated with a fiber. Each super-pixel is configured to generate a plurality of detection signals in response to detected resultant light from its associated fiber. The computing device receives the detection signals from each of the plurality of super-pixels. The computing device generates a high density-diffuse optical tomography (HD-DOT) image signal of the brain activity of the subject based on the detection signals from the super-pixels.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/519,350 filed on Apr. 14, 2017, the disclosure of which is herebyincorporated by reference in its entirety. U.S. application Ser. No.15/519,350 is a National Stage application of International ApplicationNo. PCT/US2015/056014, filed on Oct. 16, 2015, the entire disclosure ofwhich is hereby incorporated by reference as set forth in its entirety.International Application No. PCT/US2015/056014 claims the benefit ofpriority to U.S. Provisional Patent Application No. 62/065,337, filedOct. 17, 2014, the entire disclosure of which is incorporated herein byreference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH & DEVELOPMENT

This invention was made with government support under EB009233 awardedby the National Institutes of Health. The government has certain rightsin the invention.

BACKGROUND OF THE DISCLOSURE

The example embodiments herein generally relate to measuring brainactivity using diffuse optical tomography and, more specifically, to themeasuring of brain activity using super-pixel detection with wearablediffuse optical tomography.

Functional neuroimaging has enabled mapping of brain function andrevolutionized cognitive neuroscience. Typically, functionalneuroimaging is used as a diagnostic and prognostic tool in the clinicalsetting. Its application in the study of disease may benefit from new,more flexible tools. Recently, functional magnetic resonance imaging(fMRI) has been widely used to study brain function. However, thelogistics of traditional fMRI devices are ill-suited to subjects incritical care. In particular, fMRI generally requires patients to becentralized in scanning rooms, and provides a single “snap shot” ofneurological status isolated to the time of imaging, providing a limitedassessment during a rapidly evolving clinical scenario. This snap shotis generally captured on a limited basis, for example, once per stay ata hospital, once a week, once a month, and the like.

In one example, in ischemic stroke (which presents with the sudden onsetof neurological deficits), the ischemia triggers a complex cascade ofevents including anoxic depolarization, excitotoxicity, spreadingdepression, and, in some cases, reperfusion. During the hyperacute phase(first hours after onset), brain injury evolves rapidly, and therapeuticinterventions (e.g., thrombolysis/thrombectomy) aim to preserve viablebrain tissue. Beyond the hyperacute phase, potential concerns includepost-thrombolysis hemorrhagic transformation, and life-threateningcerebral edema. Therefore, throughout the hyperacute to sub-acutephases, early detection of neurological deterioration is essential andclose neurological monitoring is critical.

Diffuse optical imaging (DOI) is a method of imaging using near-infraredspectroscopy (NIRS) or fluorescence-based methods. When used to createthree-dimensional (3D) volumetric models of the imaged material, DOI isreferred to as diffuse optical tomography, whereas two-dimensional (2D)imaging methods are classified as diffuse optical topography. FunctionalNear-Infrared Spectroscopy (fNIR or fNIRS), is the use of NIRS(near-infrared spectroscopy) for the purpose of functional neuroimaging.Using fNIR, brain activity is measured through hemodynamic responsesassociated with neuron behavior.

fNIR is a non-invasive imaging method involving the quantification ofchromophore concentration resolved from the measurement of near infrared(NIR) light attenuation, temporal, or phasic changes. NIR spectrum lighttakes advantage of the optical window in which skin, tissue, and boneare mostly transparent to NIR light in the spectrum of approximately700-900 nm, while hemoglobin (Hb) and deoxygenated-hemoglobin (deoxy-Hb)are stronger absorbers of light. Differences in the absorption spectraof deoxy-Hb and oxy-Hb allow the measurement of relative changes inhemoglobin concentration through the use of light attenuation atmultiple wavelengths.

fNIR and fNIRS may be used to assess cerebral hemodynamics in a mannersimilar to fMRI using various optical techniques. In principal, fNIRScould be used for bedside monitoring of a neurological status of apatient. However, despite unique strengths, fNIRS as a standard tool forfunctional mapping has been limited by poor spatial resolution, limiteddepth penetration, a lack of volumetric localization, and contaminationof brain signals by hemodynamics in the scalp and skull.

High-density diffuse optical tomography (HD-DOT) provides an advancedNIRS technique that offers substantial improvement in spatial resolutionand brain specificity. However, these advancements in HD-DOT lead toadditional challenges in wearability and portability. For example, byincreasing a number of detection fibers in a wearable apparatus thusincreasing spatial resolution, the weight of the wearable device alsoincreases.

Accordingly, it would be beneficial to provide a new functional imagingapparatus capable of monitoring the neurological status of a patient ata bedside in a clinical setting, for example, during an acute stroke,and the like, in order to provide meaningful functional readouts usefulto a clinician. Preferably, the functional imaging apparatus would bemuch lighter in weight in comparison to previous wearable apparatuses,and be of a size that is convenient for portability, movement, andcontinuous uninterrupted wearing of the apparatus. The new bedsidemonitoring technique would benefit patients in clinical settings such asintensive care units, operating rooms, and the like.

BRIEF DESCRIPTION OF THE DISCLOSURE

One aspect of the disclosure provides an electronic console forsuper-pixel detection and analysis. The electronic console includes afiber array, a detector coupled to the fiber array, a computing devicecoupled to the detector, and a display. The fiber array includes aplurality of fibers configured to transport resultant light detected bya head apparatus worn by a subject. The detector is coupled to the fiberarray to detect resultant light from the plurality of fibers. Thedetector includes a plurality of super-pixels each defined by aplurality of pixels of an array of pixels. Each super-pixel isassociated with a fiber of the plurality of fibers. Each super-pixel isconfigured to generate a plurality of detection signals in response todetected resultant light from its associated fiber. The computing devicereceives the plurality of detection signals from each of the pluralityof super-pixels. The computing device is configured to generate a highdensity-diffuse optical tomography (HD-DOT) image signal of the brainactivity of the subject based on the plurality of detection signals fromeach of the plurality of super-pixels. The display is configured todisplay the HD-DOT image signal of the brain activity of the subject.

Another aspect of the disclosure provides a system with a wearable headapparatus configured to be worn on a head of a subject and an electronicconsole. The head apparatus is configured to direct light at the head ofthe subject and receive resultant light from the head of the subject inresponse to the light directed at the head of the subject. Theelectronic console includes a fiber array, a detector coupled to thefiber array, and a computing device coupled to the detector. The fiberarray includes a plurality of fibers configured to transport light tothe head apparatus worn by a subject and transport resultant lightreceived by the head apparatus. The detector includes a plurality ofsuper-pixels each defined by a plurality of pixels of an array ofpixels. Each super-pixel is associated with a fiber of the plurality offibers. Each super-pixel is configured to generate a plurality ofdetection signals in response to detected resultant light from itsassociated fiber. The computing device receive the plurality ofdetection signals from each of the plurality of super-pixels. Thecomputing device is configured to generate a high density-diffuseoptical tomography (HD-DOT) image signal of the brain activity of thesubject based on the plurality of detection signals from each of theplurality of super-pixels.

Another aspect of the disclosure provides a computer-implemented methodfor performing super-pixel detection using a detector that includes aplurality of super-pixels each defined by a plurality of pixels of anarray of pixels. The method is implemented by a computing device incommunication with a memory. The method includes receiving, by thecomputing device, a plurality of detection signals from the array ofpixels. For each super-pixel, a subset of the plurality of detectionsignals is associated with the super-pixel that generated the detectionsignals in the subset. A high density-diffuse optical tomography(HD-DOT) image signal of the brain activity of the subject is generatedbased at least in part on the subsets of the plurality of detectionsignals associated with the plurality of super-pixels, and the generatedHD-DOT image signal is output.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an example of a highdensity-diffuse optical tomography (HD-DOT) system;

FIG. 2 is a diagram illustrating an example of a weight-vs-coverageanalysis of a HD-DOT system;

FIGS. 3A-3F are diagrams illustrating examples of a large field-of-viewHD-DOT system for imaging distributed brain function;

FIGS. 4A-4C are diagrams illustrating examples of a super-pixel conceptand design to decrease noise and fiber size;

FIGS. 5A-5D are diagrams illustrating examples of super-pixel detection;

FIGS. 6A-6B are diagrams illustrating an example of a first light weightprototype cap;

FIGS. 7A-7E are diagrams illustrating an example of a second prototypeof a low profile, lightweight wearable HD-DOT cap;

FIG. 8 is a flow diagram illustrating an example for improving HD-DOTusing anatomical reconstructions;

FIGS. 9A-9E are diagrams illustrating examples of head surface drivensubject-specific head modeling;

FIG. 10 is a diagram illustrating an example of atlas derived DOT visualactivations on a single subject;

FIG. 11 is a diagram illustrating an example of real-timethree-dimensional (3D) object scanning;

FIGS. 12A-12D are diagrams illustrating an example of a validation offunctional diffuse optical tomography (fcDOT) in view of functionalmagnetic resonance imaging (fMRI) mapping of brain function usinglanguage paradigms;

FIGS. 13A-13F are diagrams illustrating examples of resting statefunctional connectivity diffuse optical tomography (fcDOT) maps ofdistributed resting state networks;

FIGS. 14A-14I are diagrams illustrating examples of a feasibility of aclinical HD-DOT system with limited field of view (FOV);

FIG. 15 is a diagram illustrating an example of longitudinal fcDOT maps;and

FIG. 16 is a diagram illustrating an example of a super-pixel detectionmethod for measuring brain activity.

DETAILED DESCRIPTION OF THE DISCLOSURE

The example embodiments herein relate to systems, apparatuses, andmethods for providing wearable, whole-head functional connectivitydiffuse optical tomography (fcDOT) tools for longitudinal brainmonitoring that may be used in an acute care setting, such as at abedside of a person. For example, a wearable apparatus such as a cap,helmet, and/or the like, may be used to cover the head of the person.The cap may include fibers for detecting light reflected from thebrain/head of the person. The cap may be in contact with an electronicconsole that may analyze the detected brain information. Also providedherein are systems, apparatuses, and methods for providing photometrichead modeling and motion denoising for high density-diffuse opticaltomography (HD-DOT).

According to various example embodiments, super-pixel detection enableslightweight wearable apparatuses such as caps and portable diffuseoptical tomography (DOT) instrumentation. For DOT, the size of thedetection fibers is an obstacle to fabricating more ergonomic (wearable)and portable DOT. For example, an average wearable HD-DOT apparatusincludes approximately 280 fiber strands (about one meter in length),and has a weight of around 30 pounds. Even a sparse HD-DOT wearableapparatus having between 50-100 fiber strands has a weight that isapproximately 7-10 pounds. As described in more detail, below, byintroducing super-pixel detection to DOT, smaller fibers (less thanabout 1/30 of the known standard) may be used while still maintainingHD-DOT performance specifications, and development of a novel full-headlow-profile wearable DOT cap is possible.

For example, the super-pixel detection technology may use a detectorssuch as electron-multiply charge-coupled devices (EMCCD), scientificcomplementary metal-oxide-semiconductor (sCMOS) detectors, and the like.Previously developed EMCCD-based DOT systems are slow (e.g., less thanabout 0.01 Hz) and use geometries that may require only limited dynamicrange, such as small volumes (e.g., mouse) or transmission modemeasurements.

However, functional neuroimaging DOT systems have so far not used EMCCDor sCMOS technology. In the example embodiments, a super-pixel approachuses a combination of temporal and spatial referencing along withcross-talk reduction to obtain high dynamic range (DNR) and low crosstalk. One improvement over previous technology such as avalanchephotodiodes (APDs) is a significant reduction in sensitivity (NEP) thatenables the use of smaller fibers (e.g., greater than about 30×reduction) and a smaller console (e.g., greater than about 5×).

Generally, EMCCDs and sCMOS detectors are attractive for use in DOTbecause they include many pixels, integrated cooling, electron multiplygain, A/D conversion, flexible software control, and the like. Achallenge in using EMCCDs or sCMOS detectors is to establish DOTdetector specifications, including low detection noise equivalent power(NEP<20 fW/√Hz), detectivity (3(fW/√Hz)/mm²), high dynamic range(DNR>10⁶), low inter-measurement cross talk (CT<10⁻⁶), and high framerates (FR>3 Hz). However, significant challenges exist because rawsingle-pixel EMCCD signals fail to meet DOT specification by greaterthan about 100× with DNR˜10⁴, and CT˜10⁻³.

According to the example embodiments, the super-pixel design overcomesprevious limitations of EMCCD based DOT systems and lowers the noiseequivalent power (NEP) while maintaining high-dynamic range (DNR>10⁶),low cross-talk (CT<10⁻⁶), and reasonable frame rates (FR>3 Hz). Forexample, the super-pixel concept leverages massive pixel summing whileavoiding corruption by noise sources.

For example, the super-pixel detection method may generate a mediumsized detector (scale 0.1 to 1 mm diameter) by summing pixel values on aCMOS or CCD camera. In principal, the noise equivalent power scales as˜area^(1/2), and thus (NEP/area) scales as ˜area^(1/2). As anon-limiting example, a super-pixel (area=0.13 mm²) may provide NEP=0.15fW/√Hz and (NEP/area)=1.18 (fW/√Hz)/mm². However, simple binning andtemporal summing may not be sufficient. EMCCDs have a dark-field signaldrift that becomes apparent when summing many frames. To counter this,within frame dark-field measurements and temporalmodulation/demodulation are used. By lowering the detectivity(NEP/area), the dynamic range is commensurately increased at the sametime (e.g., ˜5·10⁶).

Before cross-talk is addressed, the basic math involved is addressedwith super-pixel summing and how the summing modifies the noise floor,the detectivity, and dynamic range. A goal of super-pixel DOT (SP-DOT)is to create a wearable, whole-head imaging system. As a non-limitingexample, the whole head may include a top half of the head, a scalp ofthe head, a surface of the head from the forehead to the back neckline,and the like. To achieve this, the field-standard optical fibers aredecreased by a factor of ˜10× in diameter. This decrease in diametercauses a ˜100× decrease in weight, but also a decrease in the amount oflight collected. Currently used APD detectors are not sensitive enoughfor use with 10× smaller fibers. Detectivity (D) is a measurement ofthis sensitivity per area of incident light via the fiber.Mathematically, the Detectivity is the Noise Floor (NF) of the sensordivided by the area (A) over which the light is incident.

An advantage of using sCMOS and EMCCD sensors is that they are moresensitive than the APDs. For example, the individual pixels on a sCMOSsensors have a Noise Equivalent Power (NEP) that is 10⁴ lower than theAPDs. A potential drawback is that the individual pixels have a DynamicRange that is 10² lower than the APDs, and a smaller collection area.The Super-pixel algorithm shown below manipulates a CCD sensorindividual pixels so that its Dynamic Range is increased while loweringthe Detectivity (NEP/area).

For example, a single pixel before a super-pixel algorithm is appliedhas a noise floor (NFpix), an area (Apix), a resulting Detectivity(Dpix=NFpix/Apix), and a dynamic range (DNRpix). The DNRpix iscalculated as the full well depth of the pixel (FWpix) divided by thenoise floor: DNRpix=FWpix/NFpix.

In order to increase the dynamic range and decrease the detectivity,multiple pixels are combined together by summing N pixels within asingle frame together. Summing N pixels together increases the full welldepth linearly with N (FWsp=Fwpix*N) but the noise floor increases asthe square root of N because the noise is added in quadrature(NFsp=NFpix*√N). The dynamic range therefore increases as the squareroot of N: DNRsp=FWsp/NFsp=(Fwpix*N)/(NFpix*√N)=DNRpix*√N. While summingN pixels increases the noise floor, it also increases the resultingarea. Therefore, the detectivity will decrease by the square root of N:Dsp=NFsp/Asp=(NFpix*·N)/(Apix*N)=Dpix/√N.

By creating a super-pixel within a single frame, the dynamic range(DNRsp) has increased and the detectivity (Dsp) has decreased. Howeverto use this super-pixel algorithm for brain imaging, the sampling rateof the data needs to be considered. A typical data rate for brainimaging is 10 Hz. To compare the noise across sensors, the noise flooris calculated over a 1 second bandwidth. If the CMOS collected at aframe rate of f Hz, f images are collected per 1 second and thereforeafter summing over the 1 second bandwidth the noise floor and thedetectivity increases as the square root off:NFsp,f=NFsp*√f=NFpix*√N*√f.

In this example Dsp, f=NFsp, f/Asp=(NFpix*√N*√f)/(Apix*N)=Dpix*(√f/√N).Also, the dynamic range will be improved because “f” frames are summed:DNRsp, f=√f*DNRsp=DNRpix*√f*√N.

Another aspect to account for in brain imaging is the encoding forlocation within the data. Location is encoded in separate frames, so 1frame needs to be collected for each encoding step (K). This will havethe effect of reducing the light levels in each frame by a factor of Kor increasing the noise floor in each frame by a factor of K. Theeffective read noise will therefore increase linearly with K:NFsp,f,k=NFsp,f*K=NFpix*√N*√f*K. The resultant effective detectivitywill also increase linearly with K: Dsp,f,k=(Dpix/√N)*√f*K. The dynamicrange will not change as the number of frames summed does not change:DNRsp,f,k=DNRsp,f=DNRpix*√f*√N.

The super-pixel algorithm therefore allows for sCMOS and EMCCD sensorsto perform tomographic neural imaging by using manipulations thatincrease the dynamic range as compared to a single pixel and maintains acomparable detectivity by accounting for the frame rate, size of thesuper-pixel, and number of encoding steps.

In one example embodiment, with the super-pixel algorithm with valuesnecessary for wearable, whole-head imaging with 200 micron diameterfibers (156× smaller and lighter than the traditional fibers(Diameter=2.5 mm)), the dynamic range of the super-pixel was reducedfrom 100× to only 3× lower than the APDs and the detectivity is still10× better than the APDs (Wavelength of 690 nm, N=754 pixels, frame rateof f=10 Hz, and K=72 encoding steps).

The math in the examples above, describes the book keeping for summingacross pixels and frames assuming perfect shot noise model. The crosstalk reduction algorithm below will address the problem of cross-talk, atopic ignored in the math above.

Regarding cross-talk, CT is complex at multiple levels including opticalfocusing, and electronic sources within CCD elements, EMCCD gain, sCMOSreadout structures, and A/D conversion. A super-pixel cross-talkreduction (CTR) method may be used to leverage the unique super-pixelreference areas. A bleed pattern for each super-pixel (into othersuper-pixels) may be measured in a calibration step. During operation,scaled bleed patterns are subtracted for each super-pixel from all othersuper-pixels. The bleed pattern correction is effectively a matrixoperation that transforms a vector of raw super-pixel values to a vectorof corrected super-pixel values. When the CTR method is implemented, theCT is less than about 1·10⁻⁶.

The super-pixel concept generally involves two steps within frameincluding dark-field subtraction, and an active cross-talk reductionscheme that uses calibrated bleed patterns to remove cross-talk signalsduring operation based on the images obtained when multiple fibers areilluminated.

As described in more detail, below, a study was conducted for testingsuper-pixel feasibility. Super-pixel feasibility was tested using 0.4 mmfiber detectors. An EMCCD (Andor Tech, iXon Ultra 897) with 512×512pixels of size 16×16 μm had an EM gain at 10×. In comparing super-pixeldetectors to APDs (Hamamatsu, 3 mm dia., gain=30), NEP was evaluated at1 Hz. Dark backgrounds were subtracted in all cases. The super-pixeldetectors provide NEP=0.2 fW/√Hz, about 100× lower than the APDs, aDNR=5.10⁶ and CT<10⁶. Further, the design achieves DOT frame-rates >3Hz.

Detection fibers (400/430/730 μm core/cladding/coating, FT400EMT,Thorlabs) may be held in an aluminum block (such as a 6×6 array) andimaged onto an EMCCD. The following numbers are for a super-pixelexample with a 60-pixel diameter (total ˜2,826 pixels,magnification=2×).

Regarding frame-rate, with on camera binning (8×1) the camera FR=448 Hz.Each HD-DOT frame (4.1 Hz) will have a total 108 images (36 positionsencode steps×[two wavelengths—690 and 850 nm—plus a dark frame]). Acamera-link frame grabber with an onboard field programmable array(National Instruments NI PCIe-1473R) will compute the super-pixels inreal-time super-pixel.

FIG. 1 is a diagram illustrating an example of an HD-DOT system 100including an imaging cap 101 (sometimes referred to herein as a wearablehead apparatus), a fiber array 102, and an electronic console 110coupled with imaging cap 101 through fiber array 102.

In the example embodiment, imaging cap 101 includes a plurality ofinterconnected patches, each patch including a plurality of sources anda plurality of detectors. In the example embodiment, each sourcecorresponds with a detector to define a plurality of source-detectorpairs. During operation, the imaging cap 101 is placed over thepatient's head, and for each source-detector pair, light is transmittedto the patient by the source. Here, the transmitted light is scatteredby interactions with the patient, and at least some of the scatteredlight (sometimes referred to herein as “resultant light”) is received bydetectors. In one embodiment, imaging cap 101 is configurable for aparticular patient, e.g., by modeling the cap based on the patient'sanatomy, and is lightweight to facilitate portability of the HD-DOTsystem and enable longitudinal imaging of the patient in the acutesetting (e.g., a clinic, intensive care unit, or other environment).

Fiber array 102 may include a plurality of source fibers and a pluralityof detector fibers. Source fibers are optical imaging fibers that maytransport light from electronic console 110 to sources on imaging cap101. Similarly, detector fibers are optical imaging fibers thattransport light from detectors on imaging cap 101 to electronic console110. In some embodiments, fiber array 102 may be constructed using fewerand/or smaller optical imaging fibers to facilitate portability of theHD-DOT system 100.

In the example embodiment, electronic console 110 includes a fiber arrayholder 103, a detector 105, a lens 106 positioned between fiber arrayholder 103 and detector 105, and a light source 104 coupled with fiberarray 102 by fiber array holder 103. Fiber array holder 103 is coupledwith optical fibers (i.e., source fibers and detector fibers) of fiberarray 102, and is configured to hold the fibers in a desired arrangementto allow optical communication between fiber array holder 103, detector105, and light source 104. In the example embodiment, fiber array holder103 holds the fibers in a square arrangement that corresponds to theshape of detector 105. In other embodiments, fiber array holder 103 maybe configured to hold the optical fibers in any suitable arrangement toenable the HD-DOT system 100 to function as described herein.

Detector 105 is an image sensing device and is positioned withinelectronic console 110 to receive light from detector fibers of fiberarray 102. Detector 105 converts incident light into electron charges togenerate an electric signal that may be processed to construct, forexample, HD-DOT images of the patient or the patient's brain. In theexample embodiment, detector 105 may include an electron multiplycharge-coupled device (EMCCD) having a plurality of pixels defined on asurface of the detector. During operation, detector fibers transportlight (i.e., scattered light received by the detectors) between imagingcap 101 and electronic console 110. The light is received at theelectronic console 110 at fiber array holder 103. The received light isfocused onto detector 105 by lens 106, and the light incident ondetector 105 is converted into an electric signal including HD-DOT imagedata. In some embodiments, detector 105 may include other image sensingdevices such as, e.g., a charge-coupled device (CCD), a complementarymetal-oxide-semiconductor (CMOS), or any suitable image sensing deviceto enable the system to function as described herein.

In the example embodiment of CMOS of scientific CMOS (sCMOS), therow-by-row nature of the readout by the CMOS does introduce row-specificnoise. To remove temporal drift noise, the frame-to-frame background issubtracted first. If there were no other noise sources in the CMOS, thenoise would follow predictions of the super-pixel math above. For thesame reason the cross-talk reduction is used with the EMCCD's a similarapproach is required for sCMOS. In particular it can be of an advantageto subtract row-specific noise, wherein a number of pixels for each rowthat are not illuminated are used to generate a row specific dark value.The row specific dark values are then subtracted from the rest of thepixels in that row.

Light source 104 is positioned within electronic console 110 to providelight to source fibers of fiber array 102. In the example embodiment,light source 104 includes a plurality of laser diodes (LDs), and each LDprovides light to a source fiber and, further, to a source in imagingcap 101. In other embodiments, light source 104 includes a plurality oflight emitting diodes (LEDs). In yet other embodiments, light source 104may include any other suitable device for generating light to enable thesystem to function as described herein.

In the example embodiment, electronic console 110 also includes acomputing device 115 for processing the electric signal generated bydetector 105 to compute HD-DOT images. Computing device 115 may includeat least one memory device 150 and a processor 120 coupled to memorydevice 150. In the example embodiment, memory device 150 storesexecutable instructions that, when executed by processor 120, enablecomputing device 115 to perform one or more operations described herein.In some embodiments, processor 120 may be programmed by encoding anoperation as one or more executable instructions and providing theexecutable instructions in memory device 150.

Processor 120 may include one or more processing units (not shown) suchas in a multi-core configuration. Further, processor 120 may beimplemented using one or more heterogeneous processor systems in which amain processor is included with secondary processors on a single chip.As another example, processor 120 may be a symmetric multi-processorsystem containing multiple processors of the same type. Further,processor 120 may be implemented using any suitable programmable circuitincluding one or more systems and microcontrollers, microprocessors,programmable logic controllers (PLCs), reduced instruction set circuits(RISCs), application specific integrated circuits (ASICs), programmablelogic circuits, field programmable gate arrays (FPGAs), and any othercircuit capable of executing the functions described herein. Further,processor 120 may include an internal clock to monitor the timing ofcertain events, such as an imaging period and/or an imaging frequency.In the example embodiment, processor 120 receives imaging data fromimaging cap 101, and processes the imaging data for HD-DOT.

Memory device 150 may include one or more devices that enableinformation such as executable instructions and/or other data to bestored and retrieved. Memory device 150 may include one or more computerreadable media, such as, dynamic random access memory (DRAM), staticrandom access memory (SRAM), a solid state disk, and/or a hard disk.Memory device 150 may be configured to store application source code,application object code, source code portions of interest, object codeportions of interest, configuration data, execution events and/or anyother type of data.

Computing device 115 also includes a media display 140 and an inputinterface 130. Media display 140 is coupled with processor 120, andpresents information, such as user-configurable settings or HD-DOTimages, to a user, such as a technician, doctor, or other user. Mediadisplay 140 may include any suitable media display that enablescomputing device 115 to function as described herein, such as, e.g., acathode ray tube (CRT), a liquid crystal display (LCD), an organic lightemitting diode (OLED) display, an LED matrix display, and an “electronicink” display, and/or the like. Further, media display 140 may includemore than one media display. According to various example embodiments,the media display may be used to display HD-DOT image data on a screenthereof.

Input interface 130 is coupled with processor 120 and is configured toreceive input from the user (e.g., the technician). Input interface 130may include a plurality of push buttons (not shown) that allow a user tocycle through user-configurable settings and/or user-selectable optionscorresponding to the settings. Alternatively, input interface 130 mayinclude any suitable input device that enables computing device 115 tofunction as described herein, such as a keyboard, a pointing device, amouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touchscreen), a gyroscope, an accelerometer, a position detector, an audiointerface, and/or the like. Additionally, a single component, such as atouch screen, may function as both media display 140 and input interface130.

Computing device 115 further includes a communications interface 160.Communications interface 160 is coupled with processor 120, and enablesprocessor 120 (or computing device 115) to communicate with one or morecomponents of the HD-DOT system, other computing devices, and/orcomponents external to the HD-DOT system. Although not shown in FIG. 1,computing device 115 may further include a head modeling module thatgenerates a photometric head model of the subject, and the detector maygenerate an image signal of the brain activity of the subject based onthe photometric head model of the subject. Also, the computing device115 may further include a de-noising module that removes noise from thelight transported by the plurality of fibers from the head apparatus,and the detector may generate the image signal of the brain activity ofthe subject based on the removed noise. Embodiments of the head modelingand the noise removal are further described in various examples herein.

In the example embodiments, HD-DOT system 100 includes imaging cap 101that provides whole-head coverage and which is lightweight. FIG. 2illustrates an example of a weight-vs-coverage analysis of an HD-DOTsystem including wearable head apparatuses having different coveragesand weights. As shown in FIG. 2, at point A, sparsely populated HD-DOTsystems are lightweight (or wearable), but do not provide whole-headcoverage. In this example, wearable head apparatus 201 includes sparselypopulated fibers that, when combined, weigh approximately 8 pounds.However, due to the space between each fiber, the apparatus 201 does notprovide complete coverage of the entire head of a person.

Coverage may be increased to provide whole-head imaging by increasingthe number of source-detector pairs in the imaging cap, and increasingthe relative number of imaging fibers coupled with the imaging cap. Insuch systems, increasing the number of imaging fibers also increases theweight of the imaging cap. As shown in FIG. 2 at point B, increasingcoverage by increasing the number of imaging fibers in wearable headapparatus 202, forces the use of “hair-dryer” ergonomics forapproximately one-half head coverage (also shown in FIG. 3). In thisexample, more head coverage is provided, however, such coverage requiresabout 280 fibers (for 1 m length fibers) and weighs up to about 30 lbs.Although the wearable head apparatus in this example can be supported bystrain-relief guides, this fixes the apparatus in space and requiresspecial seating and positioning that result in the “hair-dryer”ergonomics. Moreover, the electronic console in this system typicallyrequires three 7 foot, 19 inch racks, and is essentially not portable.As a non-limiting example, a length of the fibers may be in a rangebetween zero and two meters, one half meter and one and a half meters,one meter to three meters, and the like.

As shown in FIG. 2 at point C, wearable whole-head HD-DOT 203 accordingto one or more example embodiments may be provided by reducing theweight of a wearable head apparatus (i.e. an imaging cap in thisexample) while maintaining, or enhancing, head coverage. For example,during operation, a wearable whole-head HD-DOT apparatus 203 can meet(or exceed) performance requirements imposed by the high attenuation(blood volume) of the brain with optode separations of about 1-5 cm.Performance requirements may include low noise levels (NEP<20 fW/√Hz fora 3 mm detector), high dynamic range (DNR>10⁶), and lowinter-measurement cross talk (CT<10).

In this example, a super-pixel approach to instrumentation enablesweight reduction of HD-DOT imaging caps. For example, a wearablewhole-head HD-DOT apparatus 203 is enabled, at least in part, by using asuper-pixel detection method and electron-multiply charge-coupleddevices (EMCCD). The super-pixel detection method uses a combination oftemporal and spatial referencing along with cross-talk reduction toobtain high dynamic range (DNR) and low cross talk. Furthermore, thesuper-pixel detection method provides a reduction in sensitivity (NEP)over at least some known methods and enables the use of smaller imagingfibers and a smaller console. For example, the method enables the use ofimaging fibers up to about 30 times smaller than such fibers for knownmethods, and enables the use of an electronic console up to about 5times smaller than such consoles for known methods. In one embodiment,use of smaller imaging fibers provides an imaging cap having a weight ofabout 1 lb (e.g., similar to the weight of a bicycle helmet) which, whenprovided along with an electronic console having a low-profile design,provides a wearable whole-head HD-DOT system suitable for longitudinalfcDOT in the acute care setting. In addition, whole-head HD-DOTapparatus 203 extends the field-of-view to cover multiple contiguousfunctional domains. For example, the imaging cap may have a weightbetween 0 pounds and two pounds, a range between a half pound and oneand a half pounds, and the like. As another example, the imaging cap mayhave a weight that exceeds two pounds or that is less than two pounds.

FIGS. 3A-3F are diagrams illustrating examples of a large field-of-view(FOV) HD-DOT system for imaging distributed brain function. The examplesof FIGS. 3A-3F correspond to wearable head apparatus 202 in FIG. 2. Inthe example embodiments, FIG. 3A illustrates a set-up for HD-DOT. FIGS.3B, 3C, and 3D illustrate a plurality of monitored data sets formanaging data quality control. For example, FIG. 3B illustrates theaverage light intensity for every source-destination (SD) pair separatedby <55 mm, spanning a DNR=10⁶. FIG. 3C illustrates an average lightlevel for each source and detector on a flattened view of the cap. FIG.3D illustrates measurements above the noise threshold (variance <7.5%,shown as lines). Also, FIG. 3E illustrates the FOV across 8 subjects,where color bar codes the number of subjects with usable sensitivity ata given location of cortex. FIG. 3F illustrates a subject wearing afiber head apparatus.

In the prototype HD-DOT system of FIGS. 3A-3F, DOT instrumentation forimproved cortical coverage was developed by constructing a high-densityarray including a high-density regular grid of sources and detectors.The high-density array places strong demands on hardware, specifically,high sensitivity, low noise floor, and large dynamic range of eachdetector (shown in FIG. 3b ). Development of in situ data qualitycontrol was critical for managing the large source-detector pair datasets (#SD-pairs >2000) (shown in FIGS. 3C and 3D). An imaging headapparatus was developed that couples the optodes (i.e., source anddetector fibers) to the head. However, in this example, the headapparatus is very heavy due to the size of the fibers included in thesource-detector array.

According to various example embodiments, super-pixel concepts anddesigns may be applied to the optical fibers and image sensors of theHD-DOT systems described herein, enabling a reduction in an overall sizeof the wearable head apparatus, and, thus enabling a reduction in a sizeof a computing device that the head apparatus is connected to.

FIGS. 4A-4C are diagrams illustrating examples of a super-pixel conceptapplied to an HD-DOT system to decrease noise and fiber size. Referringto FIG. 4A, the fibers are relayed to an electron multiply chargecoupled device (EMCCD) 105 using a high numerical aperture lens tomaintain high transmission (e.g., >90%). As shown in FIG. 4B, a 6×6array of super-pixels 400 is defined. Each super-pixel 450 of the arrayof super-pixels 400 includes a core region 410 used to detect thesuper-pixel light intensity, a buffer region 420 where light levelsdecay by up to 10⁴ and are discarded, and a reference region 430 tocalculate stray noise signals. Using reference subtraction, very lownoise and cross talk may be obtained (shown in FIG. 5). FIG. 4Cillustrates a front view of a fiber array holder (top) and a back viewwith optical fibers (bottom).

In the example embodiments, a super-pixel detection method may overcomeprevious limitations of CCD-based DOT systems. Also, the super-pixeldetection method may lower the noise equivalent power (NEP) relative toavalanche photodiode (APD) detection (NEP=20 fW/√Hz), while maintaininghigh-dynamic range (DNR>10⁶), low cross-talk (CT<10⁻⁶), and reasonableframe rates (FR>3 Hz). The super-pixel detection method leverages pixelsumming while reducing corruption by noise sources. When implementingthe super-pixel detection method, a cross talk reduction (CTR) methodbetween super-pixels may be performed. A study was conducted to test thefeasibility of the super-pixel detection method using 0.4 mm fiberdetectors (shown in FIGS. 4 and 5). In the study, the image sensorincluded an EMCCD with 512×512 pixels of size 16×16 μm had an EM gain at10×.

In the example embodiments herein, a super-pixel such as super-pixel 450shown in FIG. 4B includes a plurality of pixels combined to form onelarge super-pixel. In the example of FIGS. 4A-4C, a detector 400includes an array of pixels. In this example, the detector includes anarray of 510×510 pixels. Rather than analyze data from each individualpixel, the example embodiments generate the super-pixels. In thisexample, a super-pixel is 85×85 pixels. Accordingly, rather than anarray of pixels including 260,100 pixels (510×510), the array includes36 super-pixels (6×6), where each super-pixel includes 7,225 pixels. Itshould be appreciated that the example embodiments are not limited tospecific sizes of detector arrays 400 and super-pixels, and may be anydesired size.

Within each super-pixel is a pixel core 420. The pixel core may includea square shape, a circular shape, an oval shape, an elliptical shape,and the like. In various examples, to prevent noise and cross-talkbetween super-pixels, each super-pixel 450 includes a buffer 420 thatsurrounds the pixel core 410. Also, buffer 420 may be further surroundedby reference region 430. For example, the buffer 420 and the referenceregion 430 may be generated by turning off or otherwise preventing lightfrom being detected by pixels in the buffer region 420 and the referenceregion 430. In this example, the pixel core 410, the buffer region 420,and the reference region 430 may be included within the super-pixel(i.e. within the 85×85 pixels).

FIG. 5 are photographs illustrating a performance of the super-pixeldetection method (all data is shown as log(abs(data))) shown in FIG. 4.As shown in FIG. 5A, raw EMCCD images have a DNR that is determined bythe full well capacity and the readout noise for about 10⁴. As shown inFIG. 5B, simple binning into 6×6 binned regions improves the DNR to1×10⁵, but cross talk still occurs at 1×10⁻³. As shown in FIG. 5C,super-pixel analysis reduces CT<10⁻⁶ and generates a DNR of about 5×10⁶,and NEP=2 fW/√Hz. FIG. 5D illustrates a full range test showing improvedCT and DNR of super-pixels versus simple binning.

In the example of FIGS. 5A-5D, in studying the feasibility of thesuper-pixel detection method, the super-pixel detection method iscompared to a design including avalanche photodiodes (APDs) (Hamamatsu,3 mm dia., gain=30) by evaluating NEP at 1 Hz. In this example, darkbackgrounds were subtracted in all cases (shown in FIGS. 5A, 5B, and5C). Based on the comparison, the super-pixel detection method providesNEP=0.2 fW/√Hz, 100 x lower than the design including APDs, a DNR=5×10⁶,and CT<10⁶. Further, the super-pixel detection method achieves DOTframe-rates less than about 3 Hz. Yet further, the super-pixel detectionmethod also enables improvements in wearability.

According to various example embodiments, imaging caps used in variousexamples were developed using the super-pixel detection method for usein the acute setting.

FIGS. 6A-6B illustrate examples of an HD-DOT imaging cap 601. As shownin FIG. 6A, HD-DOT imaging cap 601 includes 24 source fibers and 28detector fibers. FIG. 6B illustrates an example of the elastic fiberattachment of HD-DOT imaging cap 601 for facilitating lateral stabilityand surface normal compliance. HD-DOT imaging cap 601 may be built usingsuper-pixel lightweight fibers to provide improvements in capergonomics. In HD-DOT imaging cap 601, fibers may be epoxied withinright-angle aluminum tubes and are anchored to the cap with elasticstraps that provide a “spring” effect to hold the fibers firmly yetcomfortably against the scalp (shown in FIG. 6A). The fibers and/ortubes may protrude through the cap by about 3-5 mm allowing for combingthrough hair of a person. In this example, for a whole-head wearablecap, approximately 288 fibers have a total cross-sectional area of about1.4 cm², similar to four USB cables.

FIGS. 7A-7E illustrate a second prototype HD-DOT imaging cap 701 that isa low profile, lightweight wearable HD-DOT imaging cap. As shown inFIGS. 7A and 7B, fibers of second prototype imaging cap 701 are guidedby an anatomical computer model that optimizes the placement of thefibers, and accommodates the position dependent curvature of the headsurface (which may be generated from a MRI population atlas). As shownin FIGS. 7C and 7D, a head surface is expanded to a cap 8 mm larger thanthe head, and converted to STL files which are printed in ABS plasticusing a three-dimensional (3D) printer. As shown in FIG. 7E, the patchesare integrated into a neoprene cap. Elastic fiber management from HD-DOTimaging cap 601 (shown in FIG. 6) is incorporated to optimizefiber/scalp coupling.

In designing the second HD-DOT imaging cap 701, second imaging cap 701incorporates anatomical morphology of the head (derived from MRI data)into the cap structure itself. Using an energy minimization algorithm,the full-head grid of optode positions may be relaxed onto a computermodel of a subject's head anatomy (shown in FIG. 7A) while maintainingan interlaced source and detector grid topology. The computer model isdivided into 9 patches of optodes (shown in FIGS. 7B and 7C) thatprovide local stability of the cap and assist in fiber management. Thepatches were realized with a three-dimension printer (shown in FIGS. 7Dand 7E) and were attached to a neoprene cap to provide comfort andflexibility between the patches through hinge mechanisms (shown in FIG.7E). In building the second HD-DOT imaging cap 701, the super-pixellightweight fibers de-couple the goals of cap deformation (elastic fibermanagement and neoprene patch hinges) from fiber torque (less flexibleABS plastic patches).

According to various example embodiments, an HD-DOT system includes awearable, whole-head HD-DOT for clinical based brain imaging using thesuper-pixel detection method described herein. In various examples,wearable HD-DOT includes an imaging cap weight of about 1 lb. Imagingfiber weight is largely determined by the area of the fiber for lightcollection. One of the challenges in reducing the size of a fiber ismaintaining HD-DOT specifications. For example, HD-DOT specificationsinclude low detection noise equivalent power (NEP<20 fW/√Hz, dia=3 mm,NEP/mm²˜2.8 (fW/√Hz)/mm²), high dynamic range (DNR>106), lowinter-measurement cross talk (CT<10⁻⁶), and high frame rates (FR>3 Hz).Super-pixel detection methods enable generating an about 0.4 mm diameterdetector by summing pixels on a CCD camera. Generally, EMCCDs areattractive for DOT with many pixels, integrated cooling, electronmultiply gain, A/D conversion and flexible software control. However,additional challenges exist because raw single-pixel EMCCD signals failto meet HD-DOT specifications by greater than 100× with DNR ˜10⁴, andCT˜10⁻³.

Super-pixel detection methods help solve these challenges as shown inTable 1, below, and FIGS. 4 and 5. Detection fibers (400/430/730 μmcore/cladding/coating, FT400EMT, Thorlabs) are held in an aluminum block(6×6 array) and imaged onto an EMCCD. The following numbers are for asuper-pixel with a 60-pixel diameter (total about 2826 pixels,magnification=2×). Since NEP scales as about area^(1/2), NEP per areascales as about area^(−1/2). Potentially, a super-pixel (area=0.13 mm²)provides NEP=0.15 fW/√Hz and NEP per area=1.18 (fW/√Hz)/mm². However, asshown in FIG. 5, simple binning and temporal summing is not sufficient.EMCCDs have a dark-field signal drift that becomes apparent when summingmany frames.

Within frame, dark-field measurements and temporal modulation and/ordemodulation may be used to counter signal drift. For a super-pixel(dia=0.4 mm), the effective noise/area is reduced by about 50 and thedynamic range reaches about 5×10⁶. CT is complex at multiple levelsincluding optical focusing, and electronic sources within CCD elements,EMCCD gain and A/D conversion. A super-pixel cross-talk reduction (CTR)method was developed, leveraging the unique super-pixel reference areas(shown in FIG. 4). The bleed pattern for each super-pixel (into othersuper-pixels) is measured in a calibration step. During operation,scaled bleed patterns are subtracted for each super-pixel from all othersuper-pixels. After CTR, the CT is less than 1×10⁻⁶ (shown in FIG. 5).With on-camera binning (8×1) the camera FR=448 Hz. Each HD-DOT frame(4.1 Hz) includes a total 108 images (36 position encode steps×[twowavelengths-690 and 850 nm—plus a dark frame]). A camera-link framegrabber with an onboard field programmable array (National InstrumentsNI PCIe-1473R) computes the super-pixels in real-time.

TABLE 1 Detector weight area NEP/Area Dynamic Cross Approach (lbs) (mm²)(fW/√Hz)/mm² Range talk APD 30 7.1 2.8 1E+07 1E−06 EMCCD- 1 0.13 501E+05 1E−03 binned EMCCD- 1 0.13 1.18 4.8E+06  1E−06 super-pixel

The example system includes illumination sources including laser diodes(LD), providing an about 30× increase in peak light level (60 mW vs. 2mW CW power) over light emitting diodes (LEDs). For example, individualLDs (670 nm RL67100G, 850 nm R85100G, Roithner-Lasertechnik) for eachsource position may be coupled to 200 μm fibers. On the scalp, diffusingelements provide about a 2.5 mm spot. At a 1/10⁸ duty cycle, the singlesource fluence is about 0.2 mW/mm², well below the ANSI limit (4mW/mm²).

The example system also includes an electronic console including acamera, lens, and fiber coupling block occupying about 6×8×8 inches(height×width×depth). In a 10U height (19 in rack), 144 super-pixeldetectors are included with about 5× compression compared to knownAPD-DOT (50U for 144 detectors). Illumination will use 9U. The fullsystem is about 36×48×24 inches, including a computer (control,collection, processing).

The example system further includes an imaging cap (shown in FIGS. 6Aand 7A-7E) including sub-arrays of 6×6 detectors interlaced with 6×6sources, with 36 step time encoding of the sources. The resulting foursub-arrays run concurrently since the active sources are separated by adistance greater than 2× the longest usable source-detector pair (5^(th)nearest-neighbor) distance. In developing the imaging cap, the model maybe guided by an anatomical computer model that optimizes the placementof fibers and accommodates the position dependent curvature of the headsurface (shown in FIGS. 6 and 7). To accommodate the wide range of headsizes, the HD-DOT imaging cap may be configured in small (53±2 cm),medium (57±2 cm) and large (61±2 cm) caps. The caps may be optimizedusing small pilot studies.

The example system may also include real-time displays for cap fitoptimization. According to various aspects, HD-DOT performance dependscritically on fiber/scalp coupling. To guide operator cap fit, real-timedisplays may be developed and used in both “measurement space” (shown inFIGS. 3C and 3D) and image space using a graphics processing unitcluster (e.g., an NVidia Tesla C2075 GPU cluster). For example,developed real-time displays may be used to estimate real-time imagingwithin about 1 minute of cap placement.

To test the example HD-DOT system, bench top and in vivo performancetests may be conducted. Tests with the full implementation of asuper-pixel DOT system may be used to confirm the system specifications(shown in Table 1). In vivo tests provide realistic cranial tissuestructures and subject movement. Initial prototype testing may includelongitudinal wearability testing for up to about 12 hour scan times (20minute breaks every 4 hours, shown in FIG. 15). Functional imaging inhealthy adults (N=20) for visual, auditory, and language tasks and restis enabled by methods similar to those shown in FIGS. 12A-12D, and13A-13F.

In further testing of the example HD-DOT system, the parameter space ofthe system may be analyzed to meet design goals. According to variousaspects, a strength of the system is its extensive flexibility, withregard to the pixel binning, detector size, and temporal summing, whichmay optimize the field-of-view, dynamic range and speed. Particularly,the system may be analyzed to determine the most relevant real-timedisplays for cap fit. Further, the system may be analyzed to determinethe relative importance of “sensor space” vs “image space” data withrespect to cap fit. The analysis (and other appropriate feedback) may beused to develop real-time displays for the system.

In some example embodiments, photometric head modeling and motiondenoising for high density-diffuse optical tomography (HD-DOT) may beused in at least some of the examples.

In providing wearable, whole-head HD-DOT for the acute setting usingsystems such as, e.g., the example HD-DOT system, the subject's headsurface and the position of the imaging cap are captured for registeringthe DOT data set to a model of the subject's anatomy. Some known caseshave demonstrated the advantage of using co-registered anatomical headmodelling to improve HD-DOT localization. Specifically, demonstratingthe use of individual subject anatomical MRI (shown in FIGS. 2, 3, and8). However, for the acute care setting, research quality MRIs may notbe available for many subjects, and head models may be generated bytransforming reference (or atlas) anatomy to the subject. Computing anindividual light-path model requires capturing the exterior shape of thehead and the relative location of the HD-DOT imaging cap. A photometricapproach is contemplated for efficiently capturing this data, wherenon-linear models are used to obtain an about 5 mm correspondence withfMRI. Preliminary tests of the contemplated approach show thatnon-linear registration may be used to obtain localization errors ofless than about 3 mm for reference-anatomy versus subject-MRI headmodels (shown in FIGS. 9A-9E).

The next challenge is de-noising the captured data for the subject'shead surface and the position of the cap from motion artifacts. Someknown methods, including independent component analysis (ICA) andwavelets, have been evaluated for fMRI and fNIRS, but have yet to beestablished for fcDOT. HD-DOT overlapping measurements impose aninherent structure on potential fiber movement induced error signals. Amethod is contemplated that uses HD-DOT overlapping measurements toquantify optode coupling and provide mathematical correction of the rawsignal to account for movement artifact. A study will be conducted toevaluate the contemplated method against known approaches such as ICAand wavelet approaches.

In creating DOT head modeling and spatial normalization of functionalbrain maps, improvements in instrumentation (shown in FIG. 2) promptedthe need for advancements in (i) realistic forward light modeling foraccurate HD-DOT image reconstruction and (ii) spatial normalization forvoxel-wise comparisons across subjects.

FIG. 8 are photographs illustrating example anatomical (e.g., subjectMRI) reconstructions for improving HD-DOT. As shown in FIG. 8, aprocessing pipeline for anatomically based forward light modeling andspatial normalization was developed. A study was conducted to validateboth methods in five healthy adults by direct comparison of HD-DOT vs.fMRI responses to visual stimuli (shown in FIG. 12). At the group level,the localization error of DOT relative to fMRI was about 6.1 mm.Co-registration to anatomy also enabled projection to the pial corticalsurface using Computerized Anatomical Reconstruction Toolkit (CARET), anfMRI processing tool (shown in FIG. 12).

In the acute care setting, subject MRI may be unavailable for creatinganatomically accurate head models. FIGS. 9A-9E are photographsillustrating head surface driven subject-specific head modeling forcreating anatomically accurate head models without subject MRI. As shownin FIGS. 9A-9E, reference anatomy is transformed to the subject headsurface using linear and non-linear optimizations, and the output issubject specific DOT.

In the example embodiment, as shown in FIGS. 9A-9E, anatomicallyaccurate head models are created using reference anatomy (population MRIdata) (shown in FIG. 9A) that is warped to each individual head surface(shown in FIG. 9B) using two consecutive fitting routines. The fittingroutines include a linear registration for performing global adjustments(shown in FIG. 9C), and a non-linear registration for improving localfitting (shown in FIG. 9D). In the example embodiment, input is receivedincluding the external surface of a subject's head, the location of theoptode array relative to the subject's head, and a set of anatomicalfiducials used in the registration routines. The warped atlas and theco-registered optode array are used to compute an individualized forwardlight model that is aligned with the subject's anatomical head structure(shown in FIG. 9E). The premise of atlas-based head modeling isvalidated using head surfaces extracted from subject MRI scans and bycomparing to DOT reconstructions using subject-specific anatomicalimages and to subject-matched fMRI datasets (shown in FIG. 10).

FIG. 10 is a diagram illustrating an example of atlas derived DOT visualactivations on a single subject that spatially overlap with bothindividual MRI-based DOT reconstructions and fMRI activations (thresholdat 50% maximum). In the example embodiment, the premise of atlas-basedhead modeling is validated using head surfaces extracted from subjectMRI scans and by comparing to DOT reconstructions using subject-specificanatomical images and to subject-matched fMRI datasets.

An example modeling method described herein provides photometric headmodeling for HD-DOT. Further, an example de-noising method is providedfor motion de-noising for HD-DOT.

For the acute care setting, it may be preferable to transform areference head structure (e.g., atlas) to the subject's head surfaceshape, as a subject MRI is not always available. In the example modelingmethod (shown in FIGS. 9A-9E and 10), a set of anatomical fiducialsmeasured on subject head surfaces are used to transform the referencehead structure to the subject's head surface shape. Anatomical landmarksbased on the 10/20 international system (including nasion, inion,pre-auricular points and Cz) are used for fiducials (shown in FIG. 11with red dots). Moreover, selected optodes of the imaging array (shownin FIG. 11 with blue dots) are measured. In some known systems,fiducials are measured with an RF 3D digitizer (FastTrack, PolhemusUSA). In the example modeling method, a photometric scanner (HandyScan3D, Creaform) is used, providing improved speed over those knownsystems. The photometric scanner can retrieve both the 3D coordinates ofoptodes and the surface of the subject's head. To facilitate locatingpositions, reflective targets are placed onto the optode locations andanatomical fiducials. As shown in FIG. 11, feasibility of head surfacecapture is shown using a Kinect (Microsoft) camera. As shown in FIGS.9A-9E and 10, the feasibility of using a two-step, linear thennon-linear, transform method is established using surfaces extractedfrom MRI. In some cases, the example modeling method includes FEM headmodeling for light propagation and inversion.

The accuracy of the example modeling method will be validated in controlsubjects with MRI. Performance will be evaluated in the physical spaceof the fibers (prior to image inversion) and also in image space withtask responses at the subject and group level. Photometric capture willbe evaluated against an RF 3D pen and physical rulers. It iscontemplated that locational accuracy will be better than 1 mm. Further,it is contemplated that evaluations of functional response errors willfollow some known methods. Yet further, it is contemplated that expectedlocalization errors for atlas-derived versus subject-MRI based headmodels will be than about 2 mm (shown in FIG. 10), and between HD-DOTand fMRI will be less than about 6 mm. A study of healthy subjects(N=15) may optimize and validate (vs-MRI) the example modeling method.

Referring to the contemplated example de-noising method, frequently,data from clinical populations are contaminated with noise from movementartifacts. Effective noise suppression is needed so that large amountsof potentially useful data are not discarded. The example de-noisingmethod includes a coupling coefficient (CC) motion noise removal methodthat leverages spatial structure in DOT data.

In principal, motion noise is specific to individual fibers (e.g., ahead turn will press or pull optodes to/from the head). Motion changesthe transmission to/from individual fibers and is a multiplicative noisefactor. A wearable HD-DOT system may have about 3000 SD-pairmeasurements, yet only involve 288 fibers. In the example de-noisingmethod, coupling coefficient errors are evaluated for baseline DOTreconstructions and the technique is extended to time variant data andcoupling coefficients (the method transfers directly). An estimate ofthe coupling coefficients is calculated as the mean of the first nearestneighbor measurements for each source and detector. Time variantcoupling coefficients are modeled as:I_(cor)=[C_(so)/C_(s)(t)]*[C_(do)/C_(d)(t)]*I(t), where, I(t) is asingle SD-pair intensity, I_(cor)(t) is the corrected intensity,C_(S)(t) is the source coupling coefficient, C_(d)(t) is the detectorcoupling coefficient, and C_(so) and C_(do) are the temporal mean ofC_(s)(t) and C_(d)(t), respectively.

In some cases, the example de-noising method may include noise removalmethods from fMRI and fNIRS. In particular, the example de-noisingmethod may include one of four methods having shown promise for motionartifact removal: (i) independent component analysis (ICA); (ii) waveletanalysis; (iii) “scrubbing” data (cropping corrupted segments); and (iv)polynomial spline interpolation. Work with blind source separation ofHD-DOT data suggests that ICA will also aide in noise identification andreduction.

The example de-noising method (and other noise reduction methods) willbe evaluated in normal subjects (N=15). Task and resting state data willbe collected with specific head motions (front-to-back, side-to-side,and twisting) programmed into an event design. Wireless accelerometers(G-link-LXRS, MicroStrain) will be used to measure head motion. Themethod will be assessed using four metrics: (a) the percent suppressionof known movement artifact features; (b) the CNR of activation data; (c)test-retest reliability of resting state fc; and (d) the strength ofshort-vs-long distance connections. Based on known work onsuperficial-signal regression, expected improvements in data quality arethe most dramatic when pre-correction CNR=3±2. Further, expectedtest-retest values are similar to, or better than, some known fcDOT(standard dev. of r<0.2 for homotopic connections).

Development of the methods for photometric head modeling and de-noisingmay benefit from feedback and iteration with the development ofwearable, whole-head HD-DOT and the development of functionalconnectivity metrics, described later in detail. For example, instrumentdevelopment may suggest approaches and/or demands for cap registrationand photometry. Similarly, the success or challenges in the noisereduction methods may suggest refinements and/or alternatives to capdesign. In one study, while the HandyScan 3D has accuracy specificationssufficient to meet a 1 mm goal, it is also possible that a Kineticcamera (with KinectFusion software) has sufficient resolution (shown inFIG. 11). The Kinect has the advantage of lower cost and may be moreeasily disseminated.

Studies were conducted to assess functional connectivity in healthycontrols and chronic stroke, and to assess longitudinal functionalconnectivity in the acute care setting. While the low-frequencyfluctuations in cerebral hemodynamics were detected by NIRS and reportedin 2000, the spatial evaluation of the temporal cross-correlation wasnot explored until more recently. In a known case in 2008, an example offunctional connectivity mapping using optical techniques was developedshowing the feasibility of fcDOT in adult humans. In the known case, alarge field-of-view (FOV) system was developed to make the first maps ofdistributed brain networks with fcDOT and the results were validated bycomparison against fcMRI.

In assessing functional connectivity in healthy controls and chronicstroke, fcDOT methods are developed for evaluating how similar (ordissimilar) a single subject is in comparison to a population average.Specifically, fcDOT analyses are developed by compressing the fullfc-matrix (voxel-by-voxel) down to images that assign an fc-metric toeach voxel in the brain (shown in FIG. 14). For example, previous caseshave found bilateral homotopic connectivity maps useful in mouse studiesof Alzheimer's disease. Other potentially useful fc-indices includesimilarity and asymmetry measures. To test the fcDOT methods, normativedata sets are acquired by studying healthy subjects within ananticipated age range (years 60-80). To establish the sensitivity offcDOT to brain injury it will be important to validate in a populationwith a wide dynamic range in deficits. Chronic stroke patients have alarge spectrum of temporally stable neurological deficits and thusprovide the ideal patient population to evaluate fcDOT as a surrogate ofneurological behavior exams.

In assessing longitudinal functional connectivity in the acute caresetting, fcDOT is established in acute stroke. Bedside fcDOT (or fcDOTin the acute care setting) will enable longitudinal monitoring offunctional connectivity. The wearability of fcDOT technology enableslongitudinal bedside functional mapping of brain integrity during thepost-stroke acute time window (12-72 hours) at the bedside in theintensive care unit (ICU). In validating bedside fcDOT in the ICU, fcDOTis compared to serial behavioral exams (e.g., the NIHSS). In someembodiments, the benefit of fcDOT as a brain monitoring imaging methodis demonstrated in extended 12 hour scanning. Such time windows may bedifficult or impossible with fcMRI.

In establishing fcDOT methods for mapping brain function in humans, someprevious HD-DOT experiments had been limited to visual or motor taskparadigms. To test HD-DOT imaging of distributed, multiple-order brainfunctions, a study was conducted following a known PET study and used ahierarchy of tasks to break down language into sensory (visual andauditory), articulatory (speaking), and semantic (higher ordercognitive) processes (shown in FIGS. 12A-12D).

FIGS. 12A-12D are diagrams illustrating an example of a validation offcDOT versus fMRI mapping of brain function using a plurality oflanguage paradigms. FIG. 12A illustrates validating fcDOT versus fMRI inan example of using hearing words versus other words. FIG. 12Billustrates validating fcDOT versus fMRI using reading words versusother words. FIG. 12C illustrates validating fcDOT versus fMRI usingimagined speaking versus reading. FIG. 12D illustrates validating fcDOTversus fMRI using converting verb generation versus imagined speaking.

In the examples of FIGS. 12A-12D, the contrast-to-noise-ratio (CNR,expressed as the max t-value associated with each color bar) for HD-DOTacross subjects was within a factor of 2 of the fMRI CNR, suggestingthat HD-DOT (within its FOV) has similar reproducibility to that offMRI. A goal in extending the FOV was to image distributed resting statenetworks (RSNs) (shown in FIG. 13).

FIGS. 13A-13F are diagrams illustrating seed-based correlation mapsobtained in normal volunteers for three sensory-motor and threecognitive networks, where the anatomical location of each seed is shownas a black dot. As shown in FIGS. 13A-13F, resting state functionalconnectivity of distributed networks provides a sensitive marker ofneurological dysfunction. In particular, distributed RSNs may includethe dorsal attention (DAN), fronto-parietal control (FPC) and defaultmode (DMN) networks. These fcDOT RSNs exhibit topographies similar tothose obtained non-concurrently with fcMRI (FPC, DAN, DMN).

In assessing functional connectivity, a study was conducted to test thefeasibility of a clinical HD-DOT system with limited FOV. FIGS. 14A-14Iare diagrams illustrating examples of the feasibility of a clinicalHD-DOT system with limited FOV. FIG. 14A illustrates fcDOT in the ICU ona patient recovering from an acute stroke. FIGS. 14B and 14C illustrateCT and fcDOT for a healthy subject. FIGS. 14D and 14E illustrate CT andfcDOT for a moderate stroke subject. FIGS. 14F and 14G illustrate CT andfcDOT for a severe stroke subject. As shown in FIGS. 14B, 14D, and 14F,the infarcts are represented by binary masks. The alterations in fcpatterns measured via seed-voxel maps are correlated with the severityof stroke injury. As shown in FIG. 14H, an asymmetry index, a withinsubject measure, quantifies how different the maps are on opposite sidesof the head and shows strong correlation to the NIHSS across 6 subjects.As shown in FIG. 14I, a similarity index, a between subject measure,quantifies how similar any two fc maps are and also shows a strongcorrelation to the NIHSS.

In the example embodiment, fcDOT is evaluated by validating fcDOTagainst fcMRI and neurocognitive testing in both a normal population andchronic stroke. More particularly, fcDOT is evaluated using fc-metricsthat comprehensively evaluate the connection patterns including anasymmetry index and a similarity index. These metrics will also be usedto compare fcDOT and fcMRI. A limited FOV HD-DOT system was developed totest the feasibility of imaging populations in the neonatal ICU and inthe adult ICU at the bedside of patients recovering from stroke (shownin FIGS. 14A-14I).

As shown in FIGS. 14C, 14E, and 14G, feasibility data in adult strokepatients with seeds placed in the temporal lobe display fcDOT maps whosedisruption is significantly correlated with the volume of the infarct(R²=0.87, p=0.02; N=5). The fc maps can be assessed for asymmetrybetween hemispheres within a subject and for similarity to healthytemplates. Asymmetry is calculated as the percentage difference innumber of voxels within a seed-based fc map between the left and righthemisphere. A comparison across 6 stroke subjects shows a correlationbetween a NIH Stroke Scale and asymmetry. As shown in FIG. 14h ,subjects with worse (i.e., higher) NIHSS scores demonstrate greaterasymmetry (R²=0.80, p=0.015, uncorrected). In the example embodiment,fcDOT similarity was evaluated by calculating the spatial correlationbetween seed-voxel maps for a normal subject and each stroke subject. Asshown in FIG. 14i , across the same 6 stroke subjects, the similarityindex decreased as the NIHSS increased (R²=0.95, p=0.0008, uncorrected).

In assessing functional connectivity, a study was conducted forcontinuous fcDOT in eight hour longitudinal scans in healthy subjects.

FIG. 15 illustrates longitudinal fcDOT maps taken during a period of 7hours. In determining the feasibility of imaging longitudinally, a groupof 4 healthy subjects were scanned twice, each for 8 hours continuously.The first goal was to ascertain the wearability of the imaging cap. Thesubjects wear times were as follows: Subject 1, 7 h 47 min/7 h 21 min;Subject 2, 9 h 18 min/9 h 11 min; Subject 3, 8 h 10 min/8 h 40 min;Subject 4, 8 h 13 min/8 h 39 min. The four subjects were able to wearthe cap for 8 hours (±30 min) without reporting any increased discomfortfrom the cap. This confirmed anecdotal evidence from shorter 2 hoursscans, that any discomfort is evident during the first 30 minutes ofscanning. When the initial cap fit is sufficiently optimized, the capfit is wearable long-term. As shown in FIG. 15, preliminary imageanalysis shows promising stability of fcDOT across hours.

In further assessing functional connectivity, a study was conducted todevelop fcDOT metrics for evaluating brain injury. To establish fcDOTsensitivity to brain injury a clinical population is sought with a widedynamic range of functional deficits, stable injury and the potentialfor comparisons to fcMRI and behavior assays. Chronic stroke subjectsfit these requirements.

In the study, inclusion criteria for healthy subjects (n=32) include: 1)age 50-80 years; 2) no history of neurological disorders; and 3)balanced for gender. Exclusion criteria include: HD-DOT headsetdiscomfort or any MRI contraindications.

In the study, inclusion criteria for stroke subjects (n=48) include: 1)age 50-80 years and able to obtain informed consent from patient orpatient's representative; 2) ischemic stroke (with or withoutthrombolytic therapy); 3) first time stroke; 4) patients are selected tostratify across a range of severities, NIHSS=5 to 25; and 5) time afterstroke greater than 12 months. Exclusion criteria include: 1) non-strokediagnosis; 2) intracerebral hemorrhage; 3) DOT cap discomfort; and 4)MRI contraindications.

Healthy subjects are imaged on two days with two sessions each day,including a total of one fcMRI session and three fcDOT sessions (inrandom order). Stroke subjects are also be brought in for two days, oneday fcMRI and fcDOT, the other day fcDOT and behavior testing (in randomorder). Both days are within two weeks.

During each session, subjects are scanned (fcDOT) for 1.5 hours using(i) 30 min supine resting state, (ii) 30 min supine mixture of auditorystimuli (words) and visual stimuli (flickering checkerboards), and (iii)30 min sitting 30° head-of-bed elevation.

For each subject, one 60 minute supine fMRI scan is obtained with (i) 30minutes of resting state and (ii) 30 minutes of auditory and visualstimuli for validation of the fcDOT maps. fcMRI are collected by similarmeans to those shown in FIGS. 12A-12D and 13A-13F.

In evaluating behavior of stroke subjects, neurobehavioral assessmentsare conducted by a psychometrician blinded to the imaging results tocomprehensively assess cognitive and motor deficits. Multiple cognitivedomains are evaluated (e.g., language, memory, attention, and motorfunction) using the following tests: for spatial attention, acomputerized Posner Task, recording reaction times (RTs) and accuracy;for motor, active range of motion at the wrist, grip strength,performance on the Action Research Arm Test (ARAT), speed on the NineHole Peg Test (NHPT), in pegs/second, gait speed, and FunctionalIndependence Measurement (FIM) walk item; for attention, a Posner task,Mesulam symbol cancelation test, and Behavioral inattention test (BIT)star cancellation test; for memory, the Hopkins verbal learning test(HVLT) and brief visuospatial memory test (BVMT); for language, wordcomprehension, Boston Naming Test, oral reading of sentences, stemcompletion, and animal naming.

In the study, the fc-metrics computed for both fcDOT and fcMRI includeseed-voxel maps, homotopic-fc, asymmetry-fc, and similarity-fc.Seed-voxel maps are computed using a subset of seeds from the fcMRIliterature (within DOT FOV). Homotopic-fc is computed by constructing aninterhemispheric homotopic index using every voxel in a hemisphere as aseed. In some embodiments, the homotopic connectivity metric stronglycorrelates with ischemic deficit. Asymmetry-fc is computed, for a givenfc-map, by applying a threshold to binarize the fc map (e.g., r=0.5).The asymmetry index equals the normalized difference in the number ofvoxels above threshold between the hemispheres (shown in FIGS. 14A-14I).Similarity-fc is computed using a similarity index calculated for eachvoxel (seed), and measuring the spatial correlation between any twogiven fc maps (e.g., group-vs-group and subject-vs-group) (shown inFIGS. 14A-14I).

The performance of fcDOT in normal subjects is compared to fcMRI byvalidating head models, validating fcDOT against fcMRI, and comparinghead-of-bed elevation. In validating head models, the reference headmodel is validated in the older controls. With the auditory and visualfunctional localizers, expected localization errors are 2 mm to 5 mm.Further, the fc-metrics are evaluated for the different head models atthe subject and group level.

In validating fcDOT against fcMRI, fcDOT metrics are established throughcomparison to fcMRI and test-retest. The fcDOT data are validatedthrough comparisons of between fcDOT and fcMRI at both the singlesubject and group level for the fc-metrics. In testing the reliabilityof each fcDOT metric, intra-class correlation coefficients (ICC) arecomputed for inter-session and intra-session comparisons.

In comparing head-of-bed elevation, the difference of fcDOT betweensupine and sitting 30° head-of-bed elevation is evaluated using meanssimilar to validation of fcDOT against fcMRI, described previously.While there is no precedent from fcMRI, expected differences arerelatively small, though likely detectable. In some embodiments,comparing head-of-bed elevation provides the control data for comparingthe performance of fcDOT in chronic stroke to behavior.

In other cases, the performance of fcDOT in chronic stroke is comparedto behavior. Hypothetically, fcDOT patterns in stroke patients differfrom those of healthy age matched controls (shown in FIG. 10). Theanalysis for the performance of fcDOT in normal subjects is repeated forthe stroke subject data. In addition, the behavioral metrics arecompared against fc-metrics using logistic regression analysis to testwhether behavior abnormalities in stroke are associated with fcDOTmeasures of dysfunction. Initial analysis may use global integratedneurological behavior measures (e.g., NIHSS) and global integratedmeasures of fcDOT (e.g., brain average of (dis)similarity metric). Asecondary analysis may evaluate more specific functional relationshipsbetween the behavioral domains and sub-network fcDOT metrics (e.g., theaverage within sub-network strengths of somatomotor, attention visualand default mode sub-networks, shown in FIGS. 12 and 13). From knownstudies, it is anticipated that the somatomotor and default networkscorrelate with somatomotor behavior function. A linear mixed-modelanalysis is used on both behavioral and fc-indices. Control for themultiple comparisons follows known statistical analysis of HD-DOT andMRI, and uses a cluster analysis in conjunction with a random fieldnoise model that incorporates measures of the local temporal and spatialcorrelations.

In other studies, other metrics for evaluating brain injury aredeveloped. The field of fc-network analysis is rapidly advancing, andalternatives to the proposed fc-index may arise. For example, a methodwas developed for parcellating functional architecture. Other metricsmay include measures derived from graph theory, e.g., node degree,community assignments, participation coefficient and between-nesscentrality, cortical hubs and small world connectedness and dualregression. For example, regarding the proposed head model, a referencehead may be used that incorporates anatomical aging and the shrinkage ofbrain, with age built either from a set of previous fcMRI strokesubjects.

In yet further assessing functional connectivity, an example study wasconducted to test the feasibility of longitudinal fcDOT in the ICU.Acute stroke subjects test fcDOT in an acute disease in a subjectpopulation with a wide dynamic range of functional deficits andsignificant changes over a time (hours/days). Behavioral dysfunctionranges from a complete recovery to death. Temporally, following ischemicstroke, neurological status can be highly unstable. While fcDOT mayeventually provide a more quantitative and continuous assay than currentneurological exams, in the example embodiment, the NIH stroke scale(NIHSS) is used to evaluate fcDOT.

In some cases, in the study, a first method includes using a N=32subjects, but with moderate 4 hour scans, during the first three daysfollowing stroke. In other cases, a second method may include a N=10subjects, but pilot the feasibility of extended longitudinal imagingfcDOT for up to 12 hours.

In the study, for the first method, inclusion criteria include: 1) age50-80 years and able to obtain informed consent from patient orpatient's representative; 2) ischemic stroke (with or withoutthrombolytic therapy); 3) first time stroke; 4) patients will beselected to stratify across a range of severities, NIHSS=5 to 25; 5)first HD-DOT session within 12 hours of stroke onset. Exclusion criteriainclude: 1) non-stroke diagnosis; 2) intracerebral hemorrhage onrecruitment; 3) HD-DOT headset discomfort.

In the study, for the second method, inclusion criteria include thosefor the first method, and also include patients who are under orders of24 hour bed rest (e.g., all patients receiving thrombolytics or severestrokes), and excluding patients with significant aphasia (inability tocommunicate).

All stroke patients are evaluated in the Emergency Department (ED) byneurological examination, head CT, and standard laboratory tests.Following possible intravenous tissue plasminogen activator (IV tPA)infusion or mechanical (Solitaire stentriever) thrombolysis, patientsare admitted to the Neurological-Neurosurgical ICU for post-treatmentmonitoring. The NIHSS and Glasgow Coma Scale (GCS, a 6-point clinicalscale of arousal) is obtained every 2-4 hours as part of standardpatient care.

In the study, for the first method, subjects (n=32) are imaged within 24hours of stroke onset with two additional scans, once a day, obtained onsubsequent hospital days (1-3). Using the DOT procedures previouslydescribed herein, scans last for 4 hours so that each imaging sessionspans either two or three NIHSS assessments. The reliability of fcDOTmeasures as indicators of stroke induced neurocognitive deficits is alsoevaluated.

In comparing fcDOT and NIHSS, it is hypothesized that metrics of fcdisruption correlate with NIHSS. The fc metrics are paired with theconcurrent NIHSS across all patients and time points (32 patients×3imaging sessions×2 NIHSS time points=192 comparisons) to quantify thedegree of correlation.

In detecting change in status over time, whereas the previous analysisgroups all the data together (ignores timing), the first method of theexample study tests if fcDOT can detect changes in neurological statusover time. Patient improvement may follow reperfusion; deterioration mayoccur due to a number of causes including hemorrhage or cerebral edema.In patients with deteriorating neurologic status, fcDOT measures maydegrade in parallel. This analysis leverages the multiple time epochsacquired within each subject.

In the study, for the second method, the full benefit of fcDOT as abrain monitoring imaging method is demonstrated in extended 12+ hoursscanning. Two small-scale studies are performed; first (n=5) scan for 8hours, and second (n=5) scan for 12 hours. The study is restricted tosubjects that can communicate so that the imaging cap may be removed ifneeded. Data analysis includes linear regression to NIHSS over time.

In some cases, fcDOT sensitivity may be established as an imagingbiomarker for longitudinal monitoring of neurological status, e.g., tovalidate fcDOT in relation to NIHSS. For example, a study may comparefcDOT to CT. Conn. is used to define the spatial location and extent ofinfarct.

In other cases, fcDOT may have application in the ischemic strokepopulation. For example, fcDOT metrics may herald impeding herniation.Cytotoxic cerebral edema usually occurs within days after stroke onset,and is manifested by neurological deterioration and decline in level ofarousal. Fc metrics may be able to detect early signs of edema, e.g., bycorrelating the disruption of contra-lesional local-fc with degree ofedema as measured by midline shift (mm) from CT. Further, fcDOT maypredict future functional outcome, since recent data suggests thatbilateral homotopic fc is predictive of longer term outcome. Yetfurther, when sensitivity is high, further clinical studies to assessfcDOT utility in clinical decision-making (interventional rescue therapyin patients with “failed” IV tPA, or early craniectomy in patients withimpending cerebral edema and midline shift) may be pursued.

FIG. 16 is a diagram illustrating an example of a super-pixel detectionmethod for measuring brain activity.

Referring to FIG. 16, method 1600 includes receiving a plurality ofsignals from a plurality of fibers detecting an image of a head of auser, in 1610. For example, each of the fibers may include a sourcefiber for emitting light towards the head of the user and a detectorfiber for detecting light that is incident from the head of the user. TFurthermore, the plurality of fibers may be included in a fiber array. Afirst end of the fiber array may be attached to an imaging cap that isworn on a head of the user. The other end of the fiber array may beattached to an electronic console to measure signals detected from theimaging cap while worn on the head of the user.

The method 1600 further includes performing super-pixel detection on theimage signals received from the plurality of fibers, in 1620. Forexample, rather than all pixels return individual imaging values, adetector may be divided into super-pixels. Each super-pixel may includea plurality of pixels, for example, 25×25 pixels, 40×40 pixels, 60×60pixels, 85×85 pixels, and the like. Each super-pixel may include a corethat is configured to sense light from the fibers included in the fiberarray. Pixel values of pixels included in the core may be summed. Also,the core may be of any desired shape, for example, circular, square,elliptical, and the like. A buffer region may surround the core of eachsuper-pixel. In the buffer region, light may decay thus preventingcross-talk between the super-pixels. Each super-pixel may furtherinclude a reference region that surrounds the buffer regions. Thereference region may be used to detect stray light.

The method 1600 further includes generating HD-DOT image data based onthe super-pixel detected image signals, in 1630. A detector may convertincident light into electron charges to generate an electric signal thatmay be processed and may be used to construct, for example, HD-DOTimages of the patient or the patient's brain. In the example embodiment,the detector may include an electron multiply charge-coupled device(EMCCD) having a plurality of pixels defined on a surface of thedetector. During operation, detector fibers transport light (i.e.,scattered light received by the detectors) between an imaging cap and anelectronic console. The received light may be focused onto detector by alens, and the light incident on detector may be converted into anelectric signal including HD-DOT image data

The method 1600 further includes outputting the generated HD-DOT imagedata, in 1640. For example, the HD-DOT image data may be displayed on ascreen that is electrically connected to the electronic console.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

What is claimed is:
 1. An electronic console for super-pixel detectionand analysis, the electronic console comprising: a fiber array includinga plurality of fibers configured to transport resultant light detectedby a head apparatus worn by a subject; a detector coupled to the fiberarray to detect resultant light from the plurality of fibers, thedetector including a plurality of super-pixels each defined by aplurality of pixels of an array of pixels, each super-pixel associatedwith a fiber of the plurality of fibers, each super-pixel configured togenerate a plurality of detection signals in response to detectedresultant light from its associated fiber; a computing device coupled tothe detector to receive the plurality of detection signals from each ofthe plurality of super-pixels, the computing device configured togenerate a high density-diffuse optical tomography (HD-DOT) image signalof the brain activity of the subject based on the plurality of detectionsignals from each of the plurality of super-pixels; and a displayconfigured to display the HD-DOT image signal of the brain activity ofthe subject.
 2. The electronic console of claim 1, wherein the computingdevice further comprises a head modeling module configured to generate aphotometric head model of the subject, wherein the computing device isconfigured to generate the HD-DOT image signal of the brain activity ofthe subject based at least in part on the generated photometric headmodel of the subject.
 3. The electronic console of claim 1, wherein thecomputing device further comprises a de-noising module configured toremove noise from the plurality of detection signals, wherein thecomputing device is configured to generate the HD-DOT image signal ofthe brain activity of the subject based at least in part on thedetection signals with the noise removed.
 4. The electronic console ofclaim 1, wherein the detector comprises an electron multiplycharge-coupled device (EMCCD) or a Complementarymetal-oxide-semiconductor (CMOS) sensor.
 5. The electronic console ofclaim 1, wherein each super-pixel is defined to include a core areaincluding a plurality of core pixels, a buffer area including aplurality of buffer pixels surrounding the core, and a reference areaincluding a plurality of reference pixels surrounding the buffer.
 6. Theelectronic console of claim 5, wherein the plurality of detectionsignals include core signals generated by the plurality of core pixels,buffer signals generated by the plurality of buffer pixels, andreference signals generated by the plurality of reference pixels.
 7. Theelectronic console of claim 6, wherein the computing device isconfigured to generate the HD-DOT image signal based on the coresignals, configured to discard the buffer signals, and configured tocalculate stray noise based at least in part on the reference signals.8. The electronic console of claim 1, further comprising a fiber arrayholder configured to hold the plurality of fibers in a shape thatcorresponds to a shape of the detector to direct the resultant light ofeach fiber at a different super-pixel, and a lens configured to focusthe resultant light from the plurality of fibers onto the detector. 9.The electronic console of claim 1, wherein the plurality of fiberscomprise a plurality of source fibers configured to transport light tothe head apparatus and a plurality of detector fibers configured totransport resultant light detected by the head apparatus.
 10. A systemcomprising: a wearable head apparatus configured to be worn on a head ofa subject, the head apparatus configured to direct light at the head ofthe subject and receive resultant light from the head of the subject inresponse to the light directed at the head of the subject; and anelectronic console comprising: a fiber array including a plurality offibers configured to transport light to the head apparatus worn by asubject and transport resultant light received by the head apparatus; adetector coupled to the fiber array to detect the resultant light fromthe plurality of fibers, the detector including a plurality ofsuper-pixels each defined by a plurality of pixels of an array ofpixels, each super-pixel associated with a fiber of the plurality offibers, each super-pixel configured to generate a plurality of detectionsignals in response to detected resultant light from its associatedfiber; and a computing device coupled to the detector to receive theplurality of detection signals from each of the plurality ofsuper-pixels, the computing device configured to generate a highdensity-diffuse optical tomography (HD-DOT) image signal of the brainactivity of the subject based on the plurality of detection signals fromeach of the plurality of super-pixels.
 11. The system of claim 10,wherein the wearable head apparatus weighs between one half pound andone and one half pounds.
 12. The system of claim 10, wherein thedetector comprises an electron multiply charge-coupled device (EMCCD) ora Complementary metal-oxide-semiconductor (CMOS) sensor that isconfigured to detect the resultant light from the plurality of fibers.13. The system of claim 10, wherein each super-pixel is defined toinclude a core area including a plurality of core pixels, a buffer areaincluding a plurality of buffer pixels surrounding the core, and areference area including a plurality of reference pixels surrounding thebuffer, wherein the plurality of detection signals include core signalsgenerated by the plurality of core pixels, buffer signals generated bythe plurality of buffer pixels, and reference signals generated by theplurality of reference pixels.
 14. The system of claim 13, wherein thecomputing device is configured to generate the HD-DOT image signal basedon the core signals, configured to discard the buffer signals, andconfigured to calculate stray noise based at least in part on thereference signals.
 15. The system of claim 10, wherein the plurality offibers comprise a plurality of source fibers configured to transportlight to the wearable head apparatus worn by the subject and a pluralityof detector fibers configured to transport resultant light detected bythe wearable head apparatus.
 16. A computer-implemented method forperforming super-pixel detection using a detector that includes aplurality of super-pixels each defined by a plurality of pixels of anarray of pixels, said method implemented by a computing device incommunication with a memory, the method comprising: receiving, by thecomputing device, a plurality of detection signals from the array ofpixels; associating, for each super-pixel, a subset of the plurality ofdetection signals with the super-pixel that generated the detectionsignals in the subset; generating a high density-diffuse opticaltomography (HD-DOT) image signal of the brain activity of the subjectbased at least in part on the subsets of the plurality of detectionsignals associated with the plurality of super-pixels; and outputtingthe generated HD-DOT image signal.
 17. The computer-implemented methodof claim 16, further comprising generating a photometric head model ofthe subject, wherein the generating comprises generating the HD-DOTimage signal of the brain activity of the subject is based at least inpart on the photometric head model of the subject.
 18. Thecomputer-implemented method of claim 16, further comprising removingnoise from the received plurality of detection signals.
 19. Thecomputer-implemented method of claim 16, wherein each super-pixelincludes a core area including a plurality of core pixels, a buffer areaincluding a plurality of buffer pixels surrounding the core, and areference area including a plurality of reference pixels surrounding thebuffer, wherein the plurality of detection signals include detectionsignals generated by the plurality of core pixels, detection signalsgenerated by the plurality of buffer pixels, and detection signalsgenerated by the plurality of reference pixels.
 20. Thecomputer-implemented method of claim 19, further comprising discardingthe buffer signals, calculating stray noise based at least in part onthe reference signals, and wherein the high density-diffuse opticaltomography (HD-DOT) image signal of the brain activity of the subject isgenerated based on the core signals.